Research On UAV Path Planning For Efficient Data Collection In The Context Of Internet Of Things

In recent years,with the increasing number of applications of the Internet of Things and the vigorous development of sensor devices,there is a growing demand from users for efficient acquisition of Io T data.Drones,with their flexibility,efficiency,and wide coverage,can quickly reach target locations and collect a large amount of data in a short period of time.They have been widely used in the field of data collection and have become one of the solutions to support the large-scale deployment of the Internet of Things.Improving the data collection efficiency of drones and reducing the completion time of data collection tasks has become a current research hotspot in the efficient data collection applications of the Internet of Things.This paper studies the path planning and flight strategies of drones for efficient data collection.Firstly,this paper focuses on the path planning of unmanned aerial vehicles(UAVs).A UAV path planning model considering distance balance is established,and an improved genetic algorithm based on Teaching-Learning-Based Optimization(TLBO)and Large Neighborhood Search(LNS),called TLGA,is proposed.Specifically,TLBO algorithm is used to quickly generate the optimal solution as the initial solution of the genetic algorithm,which solves the problem of random time consumption of the initial solution in the genetic algorithm.In order to avoid the genetic algorithm falling into local optima,the crossover probability P_cand mutation probability P_min the crossover and mutation process are adaptively improved.Finally,to make the algorithm more suitable for path planning in large-area environments and enhance its global optimization ability,the destruction and repair operation in LNS algorithm is introduced,enabling the improved genetic algorithm to find the globally optimal UAV traversal path.Subsequently,simulation experiments are conducted to demonstrate the improved quality of the solution obtained by the proposed algorithm.This paper then studies the flight strategy for efficient data collection by unmanned aerial vehicles(UAVs).For efficient data collection by UAVs,a multiple data rate strategy based on Lo Ra 2.4GHz(MDR)is designed,which can use four data rate modes,with data rates from low to high being DR1,DR2,DR3,and DR4.Threshold values for these rates are obtained through experiments and testing in the same experimental environment.A fixed data rate angular bisector method(ABM)strategy is then designed for selecting the turning points of UAV flight routes during data collection at a fixed data rate.MDR and ABM are then simulated and analyzed,and the completion time and flight distance of data collection tasks are compared with changes in UAV speed and terminal node data storage capacity.The results show that the flight strategies designed in this paper can improve the efficiency of UAV data collection.In addition,considering the obstacle avoidance problems that may be encountered during UAV flight,this paper also designs an improved artificial potential field method,specifically by improving the repulsive field function,and simulation results show that the improved algorithm runs quickly and can overcome the problem of unreachable targets.Finally,this paper conducts simulation and experimentation on the efficient data collection path planning of drones.Through simulation analysis under different terminal node distribution density,area scale,and data volume scale,the simulation results show that the efficient data collection path planning method proposed in this paper can effectively reduce the completion time of drone data collection tasks and improve data collection efficiency.Then,the simulation results were verified through field experiments.This research has certain application value in achieving efficient data collection in the Internet of Things and reducing the completion time of drone data collection tasks.